Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="6"><hr/></td></tr><tr class="thead"><td class="align_center" colspan="2" rowspan="2"> Weights</td><td class="align_center" colspan="4">Hidden layer</td></tr><tr class="thead"><td class="align_center">Node 1</td><td class="align_center">Node 2</td><td class="align_center">Node 3</td><td class="align_center">Node 4</td></tr><tr><td class="thead-hr" colspan="6"><hr/></td></tr><tr><td class="align_left">Output layer</td><td class="align_center">Only one neural (ridership)</td><td class="align_center"><span style="width: 62.1637ptpx;"><svg height="8.98583pt" id="M149" style="vertical-align:-0.3499308pt" version="1.1" viewbox="-0.0498162 -8.6359 62.1637 8.98583" width="62.1637pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M244 635C114 635 38 519 38 422C38 317 111 240 217 240C236 240 255 244 277 256L345 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211 89 222 149L253 320H350C441 320 443 312 441 239H471L510 434H481C455 366 448 358 360 358H260L303 572C311 613 312 615 348 615H431C509 615 522 603 532 581C544 555 546 531 545 491L574 493C583 555 592 631 593 650Z" id="g113-70"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="616" vert-adv-y="616"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,38.839,0)"><path d="M535 230V280H52V230H535Z" id="g117-33"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="587" vert-adv-y="587"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,49.376,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z" id="g113-49"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="480" vert-adv-y="480"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,55.616,0)"><path d="M153 550H386L412 615L406 623H120L82 318C104 327 142 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class="tbody-hr"/></td></tr></table></td></tr></table>

Weights for nodes in output layer and hidden layer.

Discrete Dynamics in Nature and Society

tab4

Table 4

Table 4: Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network 